CO2 Corrosion Model Validation Matrix, CO2 Corrosion Model Validation Index Score (MoVIS), CO2 corrosion models comparison, corrosion growth rate prediction, carbon dioxide corrosion prediction m...

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CO2 Corrosion Model Validation Matrix and Index Score The Need for CO2 Corrosion Model Validation

Carbon dioxide (CO2) corrosion is arecognized integrity threat worldwide. CO2corrosion modeling has been used at boththe design and operation phases of oil andgas pipelines for the prediction of internalcorrosion growth rates. Since the classiccarbon dioxide corrosion model published byC. DE WAARD and D. E. MILLIAMS in 1970s, more than a dozen of CO2 corrosion models have beendeveloped over the past 40 years. Each of the model developers has incorporated their own laboratoryand field data to produce a CO2 corrosion model.

Considerable gap exists between the model prediction and the reality [1,2,3]. An excellent overview of thedifferent CO2 corrosion models is given in reference [3]. Some CO2 model developer claims that its modelcan "accurately" predict this and "accurately" predict that but when it comes to the corrosion rateprediction, it simply fails and it fails badly. The two figures below show comparisons of the measuredcorrosion growth rate and the corrosion growth rates predicted from thirteen CO2 corrosion predictionmodels under two specific field conditions [2]. Some CO2 corrosion models consistently underestimate theCO2 corrosion rate under most operating conditions by a factor of over 10 in some cases (Models "F" and"J" in the Figures below)! Some CO2 corrosion models consistently overestimate the CO2 corrosion rate(Models "C" and "K" in the figures below). Other models simply fail to give reasonable predictions whenthe operating conditions change (Models "B', "D" and "G" in the figures below). When a model failed topredict the corrosion rate, it failed. period. Explaining the failure to predict by saying the model is sensitiveto pH, sensitive to oil wetting, sensitive to shear stress so on and so forth is completely irrelevant to theend users. It is nothing but the final corrosion rate predicted by a CO2 corrosion model that matters to theend users. A model's ability to "accurately" predict pH, the effects of oxygen, NaCl, bicarbonate, H2S, HAc,scaling, oil wetting, fluid velocity, and any other factors has absolutely no use if the model consistently failsto make a reasonable prediction of the actual corrosion rate.

CO2 Corrosion Model Validation Matrix, CO2 Corrosion Model Validation Index Score (MoVIS), CO2 corrosion models comparison, corrosion growth rate prediction, carbon dioxide corrosion prediction m...

Figure 1 above shows that four out of the thirteen CO2 corrosion prediction models produced reasonablecorrosion growth rates while the majority of the CO2 prediction models simply failed to producemeaningful results. Under another specific field condition (Figure 2 below), all models failed to producereasonable corrosion rates. Contractors or consultants who have been using a single CO2 corrosionmodeling software for all clients and under all operating conditions may not realize the considerable, andsometimes shocking uncertainties in the predicted corrosion growth rates (by a factor of over 10!) .Facility owners and users of CO2 corrosion model software should protect their interest by validating theCO2 corrosion model software independently.

Without validation, facility owners and users of CO2 corrosion modeling programs have no way of knowingthe 'accuracy' of the predicted corrosion growth rates. The blind "trust" in a single CO2 corrosion modelwithout validation and the subsequent use of the modeled results in the design will either expose theassets to increased integrity risk (in case of Models "F" and "J") or lead to overdesign with unnecessary useof CRAs or additional inhibitor dosage (in case of models "C" and "K"). A number of detailed real-life casestudies relating to the severe under-prediction and over-prediction are presented in the 5-day course on"CO2 Corrosion Modeling for the Prediction of Internal Corrosion in Oil & Gas Pipelines".

CO2 Corrosion Model Validation Matrix, CO2 Corrosion Model Validation Index Score (MoVIS), CO2 corrosion models comparison, corrosion growth rate prediction, carbon dioxide corrosion prediction m...

It is always easier and better to validate the CO2 corrosion modeling softwarebefore commencing a modeling project than trying to validate the modeledresults afterwards.

Commercial CO2 model software developers typically do not provide the users with any validation details. Validation of modeled results against lab or field data is often difficult as quality lab or field data under theprevailing operating conditions used in the prediction software are not readily available. This is particularlytrue at the design stage where the input parameters are often simulated or projected. Validation ofmodeled results against corrosion monitoring data in the field may not be applicable as the corrosionmonitoring data is "spot" measurement at a specific location under some uncertain local operatingconditions while the modeled results represent the "worst case" scenario in the whole system (not spotmeasurement) under the specific operating conditions. The only practical way to make sure that yourmodeled results are reasonably reliable is to validate the CO2 modeling software itself by using your ownor any 3rd party's well-defined quality lab and field data before starting the modeling project. It is criticalto use your own or any 3rd party's quality data, not the model developer's data (JIP or in-house), for thevalidation process.

A reasonable validation process must cover a wide range of input parameter values in a systematic way.Non-performing CO2 corrosion models (such as models "F", "J", "C", "K") over a wide range of operatingconditions will be positively identified and their errors of prediction are quantified. The two figures shownabove are just comparisons of some models under two specific field conditions, they are not validations ontheir own but can become part of the validation matrix data sets. The table in reference [1] summarizedthe significant performance differences among some in-house and commercial CO2 corrosion modeling

CO2 Corrosion Model Validation Matrix, CO2 Corrosion Model Validation Index Score (MoVIS), CO2 corrosion models comparison, corrosion growth rate prediction, carbon dioxide corrosion prediction m...

software used by the oil and gas industry.

The following CO2 Corrosion Model Validation Matrix (CO2MVM) and CO2 Corrosion Model ValidationIndex Score (CO2MoVIS) systems were developed by WebCorr Corrosion Consulting Services for theobjective, comprehensive and systematic validation of any CO2 Corrosion Modeling software.

The CO2 Corrosion Model Validation Matrix (CO2MVM) consists of 8 categories of input parameters in 3different input value ranges (low, medium and high), with a total of 48 data sets in the matrix. The absolutevalue of error percent, PE, in each data set is used to compute the average score, defined as the CO2Corrosion Model Validation Index Score (MoVIS), in 3 input parameter value ranges (Low, Medium, High).

The MoVIS-L, MoVIS-M and MoVIS-H scores are direct indications of a CO2 corrosion model's predictionaccuracy in the low, medium and high input parameter value ranges respectively. The overall MoVIS scoreis the average of the MoVIS-L, MoVIS-M and MoVIS-H scores, representing the absolute error percentageaveraged over the 8 input parameter categories in 3 ranges of input parameter values. The overall MoVISscore is a direct, objective, and comprehensive measure of a CO2 corrosion model's prediction accuracy.

After validating the CO2 corrosion model software, facility owners and users of the CO2 corrosion modelsoftware will know the "accuracy" or the uncertainty in the predicted results and this will lead to betterengineering and financial decisions when it comes to corrosion allowance, material selection, chemicaltreatment, CO2 removal, glycol injection, pH stabilization, and other methods for CO2 corrosion mitigation.

Table 2 shows the recommended parameter value range to be used in the CO2 Corrosion Model ValidationMatrix. It is important to note that all data sets used in the matrix must be of high quality. CO2 corrosionmodeling follows the same "garbage in, garbage out" rule. If high quality data set is not available in someboxes in the matrix, leave the boxes blank and exclude them in the computation of the MoVIS score. Lowquality data should never be used in the validation matrix.

"High quality data" should meet the following criteria:

The lab or field data must be from a reliable and reputable source and must be verifiable with a clearand detailed description of the source, history and the background information relating to the data.The lab or field data must be complete and have detailed information on the operating/testconditions, and the test/measurement procedures/techniques used to obtain the data. Incompletedata should not be used in the validation matrix.The lab or field data from, and/or used by, the CO2 corrosion model developer should not be used inthe validation matrix.

CO2 Corrosion Model Validation Matrix, CO2 Corrosion Model Validation Index Score (MoVIS), CO2 corrosion models comparison, corrosion growth rate prediction, carbon dioxide corrosion prediction m...

Table 1 WebCorr's CO2 Corrosion Model Validation Matrix and Index Score SystemInput parameter range Low Input Range Medium Input Range High Input Range

Input parameter category L1 L2 M1 M2 H1 H2Partial pressure of CO2, pCO2 PE (pCO2,L1) PE (pCO2,L2) PE (pCO2,M1) PE (pCO2,M2) PE (pCO2,H1) PE (pCO2,H2)

Temperature, Temp PE (Temp, L1) PE (Temp, L2) PE (Temp, M1) PE (Temp, M2) PE (Temp, H1) PE (Temp, H2)

pH PE (pH, L1) PE (pH, L2) PE (pH, M1) PE (pH, M2) PE (pH, H1) PE (pH, H2)

Liquid velocity, VL PE (VL, L1) PE (VL, L2) PE (VL, M1) PE (VL, M2) PE (VL, H1) PE (VL, H2)

Partial pressure of H2S, pH2S PE (pH2S, L1) PE (pH2S, L2) PE (pH2S, M1) PE (pH2S, M2) PE (pH2S, H1) PE (pH2S, H2)

Organic acids (HAc+Ac), HAc PE (HAc, L1) PE (HAc, L2) PE (HAc, M1) PE (HAc, M2) PE (HAc, H1) PE (HAc, H2)

Bicarbonate, HCO3- PE (HCO3-, L1) PE (HCO3-, L2) PE (HCO3-, M1) PE (HCO3-, M2) PE (HCO3-, H1) PE (HCO3-, H2)

Chlorides, CL PE (CL, L1) PE (CL, L2) PE (CL, M1) PE (CL, M2) PE (CL, H1) PE (CL, H2)

AVG of Percent Error PE (AVG, L1) PE (AVG, L2) PE (AVG, M1) PE (AVG, M2) PE (AVG, H1) PE (AVG, H2)

Model Validation Index Score, MoVIS

PE (AVG, L) = MoVIS-L score Minimum of 10 dada sets in the L range are required to compute the MoVIS-L score

PE (AVG, M) = MoVIS-M score Minimum of 10 dada sets in the M range are required to compute the MoVIS-M score

PE (AVG, H) = MoVIS-H score Minimum of 10 dada sets in the H range are required to compute the MoVIS-H score

Model Validation Index Score, MoVIS PE (AVG, L+M+H) = MoVIS scorePE = absolute value of percent error = |[(model predicted CorrRate)-(measured CorrRate)]|/(measured CorrRate)

MoVIS-L score: The MoVIS-L score shows the CO2 model's performance at the lower end of the input parameters.

MoVIS-M score: The MoVIS-M score shows the CO2 model's performance in the medium range of the input parameters.

MoVIS-H score: The MoVIS-H score shows the CO2 model's performance at the higher end of the the input parameters.

MoVIS score: The MoVIS score shows the CO2 model's overall performance across a wide range of input parameters.

Table 2 Recommended Parameter Range for CO2 Corrosion Model Validation MatrixInput parameter range Low Input Range Medium Input Range High Input Range

Input parameter case category L1 L2 M1 M2 H1 H2Partial pressure of CO2, bar 0.05 0.10 1.0 3.0 10.0 20.0

Temperature, oC 20.0 40 50 60 80 120pH 3.0 3.5 4.0 4.5 5.0 6.0

Liquid velocity, m/s 0.1 0.5 1.0 3.0 10.0 15.0Partial pressure of H2S, bar 2.5x10-5 2x10-4 1.5 3.0 3.5 4.0

Organic acids (HAc+Ac), ppm 10 50 100 500 1,000 2,000Bicarbonates (HCO3-), ppm 50 100 200 500 1,000 2,000

Chlorides, ppm 100 200 1,000 5,000 20,000 100,000AVG of Percent Error PE (AVG, L1) PE (AVG, L2) PE (AVG, M1) PE (AVG, M2) PE (AVG, H1) PE (AVG, H2)

Model Validation Index Score, MoVIS

PE (AVG, L) = MoVIS-L score Minimum of 10 dada sets in the L range are required to compute the MoVIS-L score

PE (AVG, M) = MoVIS-M score Minimum of 10 dada sets in the M range are required to compute the MoVIS-M score

PE (AVG, H) = MoVIS-H score Minimum of 10 dada sets in the H range are required to compute the MoVIS-H score

Model Validation Index Score, MoVIS PE (AVG, L+M+H) = MoVIS score

References 1. The 16th Nordic Corrosion Congress, 20-22nd May 2015, Stavanger, Norway Paper No.24 Avoiding Common Pitfalls in CO2 Corrosion Rate Assessment for Upstream Hydrocarbon Industries 2. 11th International Conference on Fracture 2005 (ICF11), Turin, Italy, 20-25 March 2005 paper No. 4173 CORROSION AND FITNESS FOR SERVICE3. NACE CORROSION / 2010, Paper No. 10371: CO2 CORROSION MODELS FOR OIL AND GAS PRODUCTION SYSTEMS